Event-Driven Programming The Easy Way, with peak.events.trellis

(NOTE: As of 0.7a1, many new features have been added to the Trellis API,
and some old ones have been deprecated. If you are upgrading from an older
version, please see the porting guide for details.)

Whether it's an application server or a desktop application, any sufficiently
complex system is event-driven -- and that usually means callbacks.

Unfortunately, explicit callback management is to event-driven programming what
explicit memory management is to most other kinds of programming: a tedious
hassle and a significant source of unnecessary bugs.

For example, even in a single-threaded program, callbacks can create race
conditions, if the callbacks are fired in an unexpected order. If a piece
of code can cause callbacks to be fired "in the middle of something", both that
code and the callbacks can get confused.

Of course, that's why most GUI libraries and other large event-driven systems
usually have some way for you to temporarily block callbacks from happening.
This lets you fix or workaround your callback order dependency bugs... at the
cost of adding even more tedious callback management. And it still doesn't
fix the problem of forgetting to cancel callbacks... or register needed ones
in the first place!

The Trellis solves all of these problems by introducing automatic callback
management, in much the same way that Python does automatic memory management.
Instead of worrying about subscribing or "listening" to events and managing
the order of callbacks, you just write rules to compute values. The Trellis
"sees" what values your rules access, and thus knows what rules may need to be
rerun when something changes -- not unlike the operation of a spreadsheet.

But even more important, it also ensures that callbacks can't happen while
code is "in the middle of something". Any action a rule takes that would cause
a new event to fire is automatically deferred until all of the applicable
rules have had a chance to respond to the event(s) in progress. And, if you
try to access the value of a rule that hasn't been updated yet, it's
automatically updated on-the-fly so that it reflects the current event in
progress.

No stale data. No race conditions. No callback management. That's what the
Trellis gives you.

As you can see, each attribute is updated if the other one changes, and the
show_values action is invoked any time the dependent values change... but
not if they don't:

>>> tc.C = -40

Since the value didn't change, none of the rules based on it were recalculated.

Now, imagine all this, but scaled up to include rules that can depend on things
like how long it's been since something happened... whether a mouse button was
clicked... whether a socket is readable... or whether a Twisted "deferred"
object has fired. With automatic dependency tracking that spans function
calls, so you don't even need to know what values your rule depends on, let
alone having to explicitly code any dependencies in!

Imagine painless MVC, where you simply write rules like the above to update
GUI widgets with application values... and vice versa.

And then, you'll have the tiny beginning of a mere glimpse... of what the
Trellis can do for you.

Other Python libraries exist which attempt to do similar things, of course;
PyCells and Cellulose are two. However, only the Trellis supports fully
circular rules (like the temperature conversion example above), and intra-pulse
write conflict detection. The Trellis also uses less memory for each cell
(rule/value object), and offers many other features that either PyCells or
Cellulose lack.

A trellis.Component is an object that can have its attributes automatically
maintained by rules, the way a spreadsheet is maintained by its formulas.

These managed attributes are called "cell attributes", because the attribute
values are stored in "cell" (trellis.AbstractCell) objects. Cell objects
can be variable or constant, and either computed by a rule or explicitly set
to a value -- possibly both, as in the temperature converter example!

There are five basic types of cell attributes:

Passive, Settable Values (attr() and attrs())

These are simple read-write attributes, with a specified default value.
Rules that read these values will be automatically recalculated after
the attribute is changed.

Computed Constants Or Initialized Values (make() and make.attrs())

These attributes are usually used to hold a mutable object, such as a list
or dictionary (e.g. cache=trellis.make(dict)). The callable (passed
in when you define the attribute) will be called at most once for each
instance, in order to initialize the attribute's value. After that, the
same object will be returned each time. (Unless you make the attribute
writable, and set the attribute to a new value.)

Computed, Observable Values (@compute and compute.attrs())

These attributes are used to compute simple formulas, much like those in
a spreadsheet. That is, ones that calculate a current state based on the
current state of other values. Formulas used in @compute attributes
must be non-circular, side-effect free, and cannot depend on the
attribute's previous value. They are automatically recalculated when their
dependencies change, but only if a maintenance or action-performing rule
depends upon the result, either directly or indirectly. (This avoids
unnecessary recalculation of values that nobody cares about.)

Maintenance Rules/Maintained Values (@maintain and maintain.attrs())

These rules or attribute values are used to reflect changes in state. A
maintenance rule can modify other values or use its own previous value in
a calculation. It is re-invoked any time a value it has previously used
changes, even if no other rule depends upon it. Maintenance rules can be
circular, as in the temperature converter example, as their values can be
explicitly set -- both as an initial value, and at runtime. They are also
used to implement "push" or "pull" rules that update one data structure in
response to changes made in another data structure. All side-effects
in maintenance rules must be undo-able using the Trellis's undo API.
(Which is automatic if the side-effects happen only on trellis attributes
or data structures.) But if you must change non-trellis data structures
inside a maintenance rule, you will need to log undo actions. We'll discuss
the undo log mechanism in more detail later, in the section on Creating
Your Own Data Structures.

Action-Performing Rules (@perform)

These rules are used to perform non-undoable actions on non-trellis data or
systems, such as output I/O and calls to other libraries. Like maintenance
rules, they are automatically re-invoked whenever a value they've
previously read has changed. Unlike maintenance rules, however, they
cannot return a value or modify any trellis data.

Note, by the way, that this means performing rules should never raise
errors. If they do, the changes that caused the rule to run will be rolled
back, but if any other performing rules were run first, their actions will
not be rolled back, leaving your application in an inconsistent state.

For each of the attribute types, you can use the plural attrs() form (if
there is one) to define multiple attributes at once in the body of a class.
The singular forms (except for attr()) can be used either inline or as
function decorators wrapping a method to be used as the attribute's rule.

Let's take a look at a sample class that uses some of these ways to define
different attributes, being deliberately inconsistent just to highlight some
of the possible options:

However, "maintained" attributes will be writable if you supply an initial
value, as we did in the TemperatureConverter example. (Plain attr
attributes are always writable, and make attributes can be made writable
by passing in writable=True when creating them.)

Note, by the way, that you aren't required to make everything in your program a
trellis.Component in order to use the Trellis. The Component class
does only four things, and you are free to accomplish these things some other
way if you need or want to:

It sets self.__cells__=trellis.Cells(self). This creates a special
dictionary that will hold all the Cell objects used to implement cell
attributes.

The __init__ method takes any keyword arguments it receives, and uses
them to initialize any named attributes. (Note that this is the only
thing the __init__ method does, so you don't have to call it unless you
want this behavior.)

It creates a cell for each of the object's non-optional cell attributes,
in order to initialize their rules and set up their dependencies. We'll
cover this in more detail in the next section, Automatic Activation and
Dependencies.

It wraps the entire object creation process in a @modifier, so that all
of the above operations occur in a single logical transaction. We'll cover
this more in a later section on Managing State Changes.

In addition to doing these things another way, you can also use Cell
objects directly, without any Component classes. This is discussed more
in the section below on Working With Cell Objects.

You'll notice that each time we change an attribute value, our Rectangle
instance above prints itself -- including when the instance is first created.
That's because of two important Trellis principles:

When a Component instance is created, all its "non-optional" cell
attributes are calculated after initialization is finished. That is,
if the attribute is a maintenance or performing rule, and has not been
marked optional, then the rule is invoked, and the result is used to
determine the cell's initial value.

While a cell's rule is running, any trellis cell whose value is looked at
becomes a dependency of that rule. If the looked-at cell changes later, it
triggers recalculation of the rule that "looked". In Trellis terms, we say
that the first cell has become a "listener" of the second cell.

The first of these principles explains why the rectangle printed itself
immediately: the show performer cell was activated. We can see this if we
look at the rectangle's show attribute:

>>> print r.show
None

(The show rule is a performer, so the resulting attribute value is
None. Also notice that rules are not methods -- they are more like
properties.)

The second principle explains why the rectangle re-prints itself any time one
of the attributes changes value: all six attributes are referenced by the
__repr__ method, which is called when the show performer prints the
rectangle. Since the cells that store those attributes are being looked at
during the execution of another cell's rule, they become dependencies, and the
show rule is thus re-run whenever the listened-to cells change.

Each time a rule runs, its dependencies are automatically re-calculated --
which means that if you have more complex rules, they can actually depend on
different cells every time they're calculated. That way, the rule is only
re-run when it's absolutely necessary.

By the way, a listened-to cell has to actually change its value (as determined
by the != operator), in order to trigger recalculation. Merely setting a
cell doesn't cause its observers to recalculate:

The show rule we've been playing with on our Rectangle class is
kind of handy for debugging, but it's kind of annoying when you don't need it.
Let's turn it into an "optional" performer, so that it won't run unless we ask
it to:

By subclassing Rectangle, we inherit all of its cell attribute definitions.
We call our new optional rule show so that its definition overrides the
noisy version of the rule. And, because it's marked optional, it isn't
automatically activated when the instance is created. So we don't get any
announcements when we create an instance or change its values:

>>> q = QuietRectangle(width=18, left=25)
>>> q.width = 17

Unless, of course, we activate the show rule ourselves:

>>> q.show
Rectangle((25, 0), (17, 30), (42, 30))

And from now on, it'll be just as chatty as the previous rectangle object:

>>> q.left = 0
Rectangle((0, 0), (17, 30), (17, 30))

While any other QuietRectangle objects we create will of course remain
silent, since we haven't activated theirshow cells:

>>> q2 = QuietRectangle()
>>> q2.top = 99

@compute rules are always "optional". make() attributes are optional
by default, but can be made non-optional by passing in optional=False.
@maintain and @perform are non-optional by default, but can be made
optional using optional=True.

Notice, by the way, that rule attributes are more like properties than methods,
which means you can't use super() to call the inherited version of a rule.
(Later, we'll look at other ways to access rule definitions.)

Since the aDict attribute is "optional" (make attributes are optional
by default), it wasn't initialized when the Demo instance was created. So
we were able to set an alternate initialization value. But, if we make it
non-optional, we can't do this, because the attribute will be initialized
during instance construction:

As you can imagine, the ability to create rules like this can come in handy
for debugging. Heck, there's no reason you have to print the values, either.
If you're making a GUI application, you can define rules that update displayed
fields to match application object values.

For that matter, you don't even need to define the rule in the same class!
For example:

Now, any time we change q2, it will be printed by the Viewer's view_it
rule, even though we haven't activated q2's show rule:

>>> q2.left = 66
Rectangle((66, 99), (20, 30), (86, 129))

This means that we can automatically update a GUI (or whatever else might need
updating), without adding any code to the thing we want to "observe". Just
use cell attributes, and everything can use the "observer pattern" or be a
"Model-View-Controller" architecture. Just define rules that can read from the
"model", and they'll automatically be invoked when there are any changes to
"view".

Notice, by the way, that our Viewer object can be repointed to any object
we want. For example:

See how each time we change the model attribute, the view_it rule is
recalculated? The rule references self.model, which is a value cell
attribute. So if you change view.model, this triggers a recalculation,
too.

Remember: once a rule reads another cell, it will be recalculated whenever the
previously-read value changes. Each time view_it is invoked, it renews
its dependency on self.model, but also acquires new dependencies on
whatever the repr() of self.model looks at. Meanwhile, any
dependencies on the attributes of the previousself.model are dropped,
so changing them doesn't cause the perform rule to be re-invoked any more.
This means we can even do things like set model to a non-component object,
like this:

>>> view.model = {}
{}

But since dictionaries don't use any cells, changing the dictionary won't do
anything:

>>> view.model[1] = 2

To be able to observe mutable data structures, you need to use data types like
trellis.Dict and trellis.List instead of the built-in Python types.
We'll cover how that works in the section below on Mutable Data Structures.

By the way, the links from a cell to its listeners are defined using weak
references. This means that views (and cells or components in general) can
be garbage collected even if they have dependencies. For more information
about how Trellis objects are garbage collected, see the later section on
Garbage Collection.

Sometimes it's useful to create a maintained value that's based in part on its
previous value. For example, a rule that produces an average over time, or
that ignores "noise" in an input value, by only returning a new value when the
input changes more than a certain threshhold since the last value. It's fairly
easy to do this, using a @maintain rule that refers to its previous value:

As you can see, referring to the value of a cell from inside the rule that
computes the value of that cell, will return the previous value of the cell.
(Note: this is only possible in @maintain rules.)

So far, all the stuff we've been doing isn't really any different than what you
can do with a spreadsheet, except maybe in degree. Spreadsheets usually don't
allow the sort of circular calculations we've been doing, but that's not really
too big of a leap.

But practical programs often need to do more than just reflect the values of
things. They need to do things, too.

So far, we've seen only attributes that reflect a current "state" of things.
But attributes can also represent things that are "happening", by automatically
resetting to some sort of null or default value. In this way, you can use
an attribute's value as a trigger to cause some action, following which it
resets to an "empty" or "inactive" value. And this can then help us handle the
"Controller" part of "Model-View-Controller".

For example, suppose we want to have a controller that lets you change the
size of a rectangle. We can use "resetting" attributes to do this, in a way
similar to an "event", "message", or "command" in a GUI or other event-driven
system:

A resetting attribute (created with attr(resetting_to=value) or
attrs.resetting_to()) works by receiving an input value, and then
automatically resetting to its default value after its dependencies are
updated. For example:

Notice that setting c.wider=1 updated the rectangle as expected, but as
soon as all updates were finished, the attribute reset to its default value of
zero. In this way, every time you put a value into a resetting attribute, it
gets processed and discarded. And each time you set it to a non-default value,
it's treated as a change. Which means that any maintenance or performing
rules that depends on the attribute will be recalculated (along with any
@compute rules in between). If we'd used a normal trellis.attr here,
and then set c.wider=1 twice in a row, nothing would have happen the
second time, because the value would not have changed.

Now, we could write methods for changing value cells that would do this sort
of resetting for us, but it wouldn't be a good idea. We'd need to have both
the attribute and the method, and we'd need to remember to never set the
attribute directly. (What's more, it wouldn't even work correctly, for reasons
we'll see later.) It's much easier to just use a resetting attribute as an
"event sink" -- that is, to receive, consume, and dispose of any messages or
commands you want to send to an object.

But why do we need such a thing at all? Why not just write code that directly
manipulates the model's width and height? Well, sometimes you can, but it
limits your ability to create generic views and controllers, makes it
impossible to "subscribe" to an event from multiple places, and increases the
likelihood that your program will have bugs -- especially order-dependency
bugs.

If you use rules to compute values instead of writing code to manipulate
values, then all the code that affects a value is in exactly one place. This
makes it very easy to verify whether that code is correct, because the way
the value is arrived at doesn't depend on what order a bunch of manipulation
methods are being called in, and whether those methods are correctly updating
everything they should.

Thus, as long as a cell's rule doesn't modify anything except local
variables, there is no way for it to become "corrupt" or "out of sync" with the
rest of the program. This is a form of something called "referential
transparency", which roughly means "order independent". We'll cover this topic
in more detail in the later section on Managing State Changes. But in the
meantime, let's look at how using attributes instead of methods also helps us
implement generic controllers.

The trellis.Cells() API returns a dictionary containing all active cells
for the object. (We'll cover more about this in the section below on Working
With Cell Objects_.) You can then access them directly, assigning them to
other components' attributes.

Assigning a Cellobject to a cell attribute allows two components to
share the same cell. In this case, that means setting the .increase
and .decrease attributes of our Spinner objects will set the
corresponding attributes on the rectangle object, too:

A shared cell is a shared cell: it doesn't matter which "direction" you share
it in! It's a simple way to create an automatic link between two parts
of your program, usually between a view or controller and a model. For
example, if you create a text editing widget for a GUI application, you can
define a value cell for the text in its class:

And then you'd write some additional code to automatically set self.text
when there's accepted input from the GUI. An instance of this editor can then
either maintain its own text cell, or be given a cell from an object whose
attributes are being edited.

This allows you to independently test your models, views, and controllers, then
simply link them together at runtime in any way that's useful.

The new_high rule runs whenever value changes, and checks to see
if it's greater than the current highest value. If so, it returns true and
updates the maximum value. Let's try it out:

>>> hd = HighDetector()
>>> hd.value = 7
New high
>>> hd.value = 9

Oops! We set a new high value, but the monitor rule didn't detect a new
high, because new_high was already True from the previous high.

Just as with a regular attribute, rules normally return what might be called
"continuous" or "steady state" values. That is, their value remains the same
until something causes them to be recalculated. In this case, the second
recalculation of new_high returns True, just like the first one...
meaning that there's no change, and thus the performing rule isn't triggered.

But, just as with regular attributes, @compute and @maintain rules
can be made "resetting", using the resetting_to= keyword, allowing the
value to reset to a default as soon as all of the value's listeners have
"seen" the original value. Let's try a new version of our high detector:

Over the course of this tutorial, we've created a whole bunch of different
objects, like the temperature converter, high detector, changeable rectangle,
and a simple viewer. Let's link them up together to make a rectangle that
gets wider and taller whenever the Celsius temperature reaches a new high:

Crazy, huh? None of these components were designed with any of the others in
mind, but because they all "speak Trellis", you can link them up like building
blocks to do new and imaginative things.

By the way, although in this demonstration we saw the three outputs in one
particular order, in general the Trellis does not guarantee what order rules
will be recalculated in, so it's unwise to assume that your program will
always produce results in a certain order, unless you've taken steps to ensure
that it will.

That's why managing the order of Trellis output (and dealing with state changes
in general) is the subject of our next major section.

Time is the enemy of event-driven programs. They say that time is "nature's
way of keeping everything from happening at once", but in event-driven programs
we usually want certain things to happen "all at once"!

For example, suppose we want to change a rectangle's top and left
co-ordinates:

Oops! If we were updating a GUI like this, we would see the rectangle move
first down and then sideways, instead of just going to where it belongs in one
movement.

Therefore, in most practical event-driven systems, certain kinds of changes
are automatically deferred, usually by adding them to some kind of event queue
so that they can happen later, after all the desired changes have happened.
That way, they don't take effect until the current event is completely
finished.

The Trellis actually does something similar, but its internal "event queue" is
automatically flushed whenever you set a value from outside a rule. If you
want to set multiple values, you need to use a @modifier function or
method like this one, which we could've made a method of Rectangle, but
didn't:

Notifications of changes made by a modifier do not take effect until the
outermost active modifier function returns. (In other words, if one
modifier directly or indirectly calls another modifier, the inner
modifier's changes don't cause notifications to occur until the same time
as the outer modifier's changes do.)

Now, notice that this means that within a modifier, you can't rely on any
values controlled by rules to be updated when you make changes. This means
it's generally a bad idea for a rule to look at what it's changing. For
example:

The first print is from inside the rule, showing that from the rule's
perspective, the bottom/right co-ordinates are not updated to reflect the
changed top/left co-ordinates. The second print is from a perform rule,
showing that the values do get updated after the modifier has exited.

The reason that time is the enemy of event driven programs is because time
implies order, and order implies order dependency -- a major source of bugs
in event-driven and GUI programs.

Writing a polished GUI program that has no visual glitches or behavioral quirks
is difficult precisely because such things are the result of changes in the
order that events occur in.

Worse still, the most seemingly-minor change to a previously working version of
such a program can introduce a whole slew of new bugs, making it hard to
predict how long it will take to implement new features. And as a program
gets more complex, even fixing bugs can introduce new bugs!

Indeed, Adobe Systems Inc. estimates that nearly half of all their reported
desktop application bugs (across all their applications!) are caused by such
event-management problems.

So a major goal of the Trellis' is to not only wipe out these kinds of
bugs, but to prevent most of them from happening in the first place.

And all you have to do to get the benefits, is to divide your code three ways:

Input code, that sets trellis cells or calls modifier methods, but does not
run inside trellis rules. This kind of code is usually invoked by GUI or
other I/O callbacks, or by top-level non-trellis code.

Processing rules that compute values, and/or make undo-able changes to cells
or other data structures. (i.e. @compute and @maintain rules.)

Output rules that send data on to other systems (like the screen, a socket,
a database, etc.). This code may appear in @perform rules, or it can be
"application" code that reads results from a finished trellis calculation.

The first and third kinds of code are inherently order-dependent, since
information comes in (and must go out) in a meaningful order. However, by
putting related outputs in the same performer (or non-trellis code), you can
ensure that the required order is enforced by a single piece of code. This
approach is highly bug-resistant.

Second, you can reduce the order dependency of input code by making it do as
little as possible, simply dumping data into input cells, where it can be
handled by processing rules. And, since input controllers can be very generic
and highly-reusable, there's a natural limit to how much input code you will
need.

By using these approaches, you can maximize the portion of your application
that appears in side effect-free (or at least undo-able) processing rules,
which the Trellis makes 100% immune to order dependencies. Anything that
happens in Trellis rules, happens instantaneously, in a logical sense. Ther
is no "order", and thus no order dependency.

In truth, of course, rules do execute in some order. However, as long as the
rules don't do anything but compute their own values, then it cannot matter
what order they do it in. (The trellis guarantees this by automatically
recalculating rules whenever their dependencies change, and undoing any
calculations that "saw" out-of-date or inconsistent values.)

If you care what order some modifications to a trellis data structure occur in,
then code them both in the same maintenance rule. If you care what order two
"outside world" side-effects happen in, code them both in the same perform
rule.

For example, in the TempConverter demo, we had a performer that printed the
Celsius and Fahrenheit temperatures. If we'd put those two print statements
in separate rules, we'd have had no control over the output order; either
Celsius or Fahrenheit might have come first on any given change to the
temperatures. So, if you care about the relative order of certain output or
actions, you must put them all in one rule. If that makes the code too big
or complex, you can always refactor to extract computing or maintenance rules
to calculate the intermediate values. (Just don't put any of the external
actions in the other rules, only the calculations. Then have a perform rule
that only does the external actions.)

If you set a value from more than one place, you are introducing an order
dependency. In fact, if you set a cell value from more than one rule, the
Trellis will stop you, unless the values are equal. For example:

This example fails because the two rules set different values for the value
attribute, causing a conflict error. Since the rules don't agree, the result
would depend on the order in which the rules happened to run -- which again
is precisely what we don't want in an event-driven program!

So this rule is for your protection, because it makes it impossible for you to
accidentally set the same thing in two different places in response to an
event, and then miss the bug or be unable to reproduce it because the second
change masks the first!

Instead, what happens is that assigning two different values to the same cell
in response to the same event always produces an error message, making it
easier to find the problem. Of course, if you arrange your input code so that
only one piece of input code is setting trellis values for a given event, or
only one piece of code ever modifies a given cell or data structure, then
you'll never have this problem.

Of course, if all of your code is setting a cell to the same value, you won't
get a conflict error either. This is mostly useful for e.g. receiver cells
that represent a command the program should do. If you have GUI input code
that triggers a command by setting some receiver to True whenever that
command is selected from a menu, invoked by a keyboard shorcut, or accessed
with a toolbar button click, then it doesn't matter which event happens or
even if all three could somehow happen at the same time, because the end result
is exactly the same: the receiver processes the True message once and then
discards it.

If your rules only set cell values or modify trellis-managed data structures,
you don't need to worry about undo logging, as it's taken care of for you.

However, if you implement any other kind of side-effects in a maintenance rule
(such as updating a mutable data structure that's not trellis-managed), you
must record undo actions to allow the trellis to roll back your rule's
action(s), in the event that it must be recalculated due to an order
inconsistency, or if an error occurs during recalculation. If you don't do
this, you risk corrupting your program's state. This is especially important
if you are creating a new trellis-managed data structure type.

In general, it's best to keep side-effects in rules to a minimum, and use only
cells and other trellis-managed data structures. And of course, any side
effects that can't easily be undone should be placed in a @perform rule, which
is guaranteed to run no more than once per overall recalculation of the trellis.

However, if you are creating your own trellis-managed data structure type, you
may need to use the trellis.on_undo() API to register undo callbacks, to
protect your data structure's integrity. See the section below on Creating
Your Own Data Structures for more details on how this works.

Here's what's happening: first, v2 is calculated as 2*2==4. Then,
the update rule sets v1 to 3. However, v2 is NOT immediately
updated. Instead, it's put on a schedule of rules to be re-run. So the
update rule still sees the OLD value of v2.

So, if you are making any kind of changes from inside a rule, beware of trying
to read anything that might be affected by those changes, as you will likely
see something that's out of date. This is particularly important when changing
trellis-managed data structures, since many data structures rely on rules for
their internal consistency. So if you first write and then read the same data
structure from a single rule, you will almost certainly see inconsistent
results.

So far, all of our Trellis examples have worked with atomic cell values, like
integers, strings, and so forth. We've avoided working with lists, sets,
dictionaries, and similar structures, because the standard Python
implementations of these types can't be "observed" by rules, which means that
they won't be automatically updated.

But this doesn't mean you can't use sets, lists, and dictionaries. You just
need to use Trellis-managed ones. (Of course, all the warnings above about
changing values still apply; just because you're modifying something other
than attributes, doesn't mean you're not still modifying things!)

The Trellis package provides three primary mutable types for you to use in your
components: Set, List, and Dict. You can also subclass them or
create your own mutable types, as we'll discuss in a later section. (And, the
peak.events.collections module also provides some fancier data structures;
see the Collections manual for details.)

The trellis.Dict type looks pretty much like any dictionary, but it can
be observed by rules. Any change to the dictionary's contents will result
in its observers being recalculated. For example, if we use our view
object (defined way back in the section on Model-View-Controller and the
"Observer" Pattern), we can print it whenever it changes, no matter how it
changes:

Unlike normal values, however, even changing a dictionary entry to the same
value will trigger a recalculation:

>>> d['a'] = 2
{'a': 2}

This is because the Dict type doesn't try to compare the values you put
into it. If you need to prevent such recalculations from happening, you can
always check the dictionary contents first, or create a subclass and override
__setitem__ (but be sure to read the section on Creating Your Own Data
Structures for some important information first).

In addition to these basic features, the Dict type provides three receiver
attributes (added, changed, and deleted) that reflect changes
currently in progress. Ordinarily, they are empty dictionaries, but while a
change is taking place they temporarily become non-empty. For example:

Remember: the trellis wants all changes to be deferred until the next
recalculation. That means you can't see the effect of a change in the same
moment during which you make the change, so operations like pop() are
disallowed, because they would have to return the same value no matter how
many times you called it during the same recalculation! (Otherwise, the
change hasn't really been deferred.)

This limitation also applies to the pop() method of List and Set
objects, as we'll see in the next two sections.

Remember: as with trellis.Dict, operations like pop() are disallowed
here because they would require reading the effect of a change, before the
logical future moment in which the change actually takes effect.

trellis.List objects also have a receiver attribute called changed.
It's normally false, but is temporarily True during the recalculation
triggered by a change to the list. But as with all receiver attributes, you'll
never see a value in it from non-rule code:

>>> myList.changed
False

Only in rule code will you ever see it true, a moment before it becomes false:

Remember: as with trellis.Dict and trellis.Set, operations like
pop() are disallowed here because they would require reading the effect of
a change, before the logical future moment in which the change actually takes
effect.

trellis.List objects also have some inherent inefficiencies due to the wide
variety of operations supported by Python lists. While trellis.Set
and trellis.Dict objects update themselves in place by applying change
logs, trellis.List has to use a copy-on-write strategy to manage updates,
because there isn't any simple way to reduce operations like sort(),
reverse(), remove(), etc. to a meaningful change log. (That's why
it only provides a simple changed flag.)

So if you need to use large lists in an application, you may be better off
creating a custom data structure of your own design. That way, if you only
need a subset of the list interface, you can implement a changelog-based
structure. For example, the Trellis package includes a SortedSet type
that maintains an index of items sorted by keys, with a cell that lists
changed regions. (See the Collections manual for more details.)

A trellis.Pipe is a little bit like a Python list, except it only has
supports for 5 methods: append, extend, __iter__, __len__,
and __contains__. Its purpose is to allow you to easily interconnect
components that communicate streams of objects or data, not unlike an operating
system pipe. You can use append() and extend() to put data in the
pipe, and use the other methods to get it back out. And it resets itself to
being empty after all of its observers have had a chance to see the contents:

One common use for pipes is to allow you to create objects that communicate
via sockets or other IPC. If you write a component so that it expects to
receive its inputs via one pipe, and sends output to another, then those pipes
can be connected at runtime to a socket. And at test time, you can just
append data to the input pipe, and have a performer spit out what gets written
to the output pipe.

The Pipe type is the trellis's simplest data structure type -- so you may
want to have a peek at its source code after you read the next section. (Better
still, try to write your ownPipe clone first, and then compare it to the
real one!)

If you want to create your own data structures along the lines of Dict,
List, and Set, you have a few options. First, you can just build
components that use those existing data types, and use @modifier methods
to perform operations on them. (If you just directly perform operations, then
listeners of your data structure may be recalculated in the middle of your
changes, and see an inconsistent state.)

Depending on the nature of the data structure you need, however, this may not
be sufficient. For example, when you perform multiple operations on a
trellis.Dict, the later operations need to know about changes made by the
earlier ones. If you add some items and then delete one, for example, the dict
needs to know whether the item you're deleting is one of the ones that you
added.

But, if you use normal read operations on the dictionary (like .has_key()),
these will only reflect the "before" state -- what the dictionary had in it
during the current recalculation, before any new changes were made.

So, the Trellis-supplied data types use a couple of special tools to allow them
to "see the future" (and change it).

Let's suppose that we're creating a simple "queue" type, that keeps track of
items added to it. Its output is a list of the most-recently added items,
and the list becomes empty in the next recalculation if nobody adds anything to
it:

Let's break down the pieces here. First, we create a "todo" cell. A todo
cell is basically a resetting_to attribute, except that it resets to a
calculated value instead of a constant. It takes a function or type, just
like make. That is, if you use a function (or other object with a
__get__ method), it's called with the object the attribute belongs to,
and if you use a type (or other object lacking a __get__ method), it's
called with no arguments.

When the "todo" cell is created, the rule is called to create the resetting
value, just as with a make attribute. Unlike a make attribute,
however, its rule will be called again each time a "future" (i.e. modified)
value is required.

(By the way, you can define todo cells with either a direct call as shown
above, a @trellis.todo decorator on a function, or by using
trellis.todos(attr=func, ...)` in your class body.)

The second thing that we did in this class above is create a "future" property.
Todo cell descriptors have a .future attribute that returns a new property.
(This property accesses the "future" version of the todo cell's value --
causing the rule to be called to generate a new value, and various undo-log
operations to be performed.)

Next, we define a modifier method, add(). This method accesses the
to_add attribute, thereby getting the future value of the items
attribute. This future value is initially created by calling the "todo" cell's
rule. In this case, the rule returns an empty list, so that's what add()
sees, and adds a value to it.

(Note, by the way, that you cannot access future values except from inside a
@modifier function.)

Next, let's create another @modifier that adds more than one item to the
to_add attribute. This will works because only a single "future value" is
created during a given recalculation sweep, and @modifier methods guarantee
that no new sweeps can occur while they are running. Thus, the changes made in
the modifier won't take effect until it returns:

Finally, notice that after each change, the queue resets itself to empty,
because the default value of the items cell is the empty list that was
created when the cell was initialized.

Of course, since "todo" attributes are automatically resetting, what we've
seen so far isn't enough to create a data structure that actually keeps any
data around. To do that, we need to combine "todo" attributes with a rule to
maintain an existing data structure:

This version is very similar to the first version, but it separates added
from items, and the items rule is set up to compute a new value that
includes the added items. (Notice also the use of the make keyword to
initialize items to an empty list before the items rule is run for the
first time.)

Notice, by the way, that the items rule returns a new list every time
there is a change. If it didn't, the updates wouldn't be tracked:

Why are no updates displayed here? Because items is being modified
in-place, and when the trellis compares the "before" and "after" versions of
its value, it concludes they are the same. This didn't happen when we
returned a new list, because the old list still had its old contents, and the
new list was different.

If you are modifying a return value in place like this, you should use the
the trellis.mark_dirty() API to flag that your return value has changed,
even though it's the same object. In addition, you should log an undo action
so that if the trellis needs to roll back some calculations involving your data
structure, it can do so:

As you can see, calling mark_dirty() caused the trellis to notice the
change to the list, even though the newly-returned list is (by definition)
still equal to the previous value of the rule (i.e., the same list).

The on_undo() function lets you register a callback function (with optional
positional arguments) that will be invoked if the trellis needs to roll back
changes due to an error, or due to an out-of-order calculation. (If a rule
makes a change to a data structure that has already been read by another rule,
the trellis has to undo any changes made by the earlier rule and re-run it to
ensure consistent results.)

Registered functions are called in reverse order, so that callbacks registered
by later on_undo() calls will run before earlier ones. The Trellis keeps
track of what callbacks were registered during each rule's execution, so that
it can roll back the minimum number of changes needed to resolve a calculation
order conflict. In the event of an error, however, all changes are rolled
back:

This example is a bit odd, because it's somewhat difficult to force the trellis
to get an error in such a way as to test your undo logging. If we had simply
raised an error in the modifier, the change would appear to have been rolled
back, when in fact it hadn't happened yet! (It's easy to see this if you add
a "print" to the items rule -- if you raise an error in the modifier, it
will never be called, because the rules don't run until the modifier is over.)

So to actually test the undo-ing, we have to raise the error in a new performer
cell, which then runs after q.items is updated. (Performers don't run
until/unless there are no other kinds of rules pending.)

In later sections on Working with Cell Objects, we'll see more about how to
create and use one-off cells like this Performer, without needing to
make them part of a component.

In the meantime, please note that creating good trellis data structures can be
tricky: be sure to write automated tests for your code, and verify that they
actually test what you think they test. This is one situation where it's
REALLY a good idea to write your tests first, and try to make them fail
before you add any mark_dirty() or on_undo() calls to your code.
Otherwise, you won't be sure that your tests are really testing anything!

Of course, you don't need to deal with mark_dirty() and undo() at all,
if you stick to using immutable values as a basis for your data structure, or
use a copy-on-write approach like that shown in our Queue2 example above.
Such data structures are less efficient than updating in-place, if they contain
large amounts of data, but not every data structure needs to contain large
quantities of data!

Therefore, we suggest that you start with simpler data structures first, and
only add in-place updates if and when you can prove that the data copying is
unacceptable overhead, since such updates are harder to write in a
provably-correct way. (Note, too, that Python's built-in data types can
often copy data a lot faster than you'd expect...)

Throughout the main tutorial, we worked only with component attributes. But
it's also possible to work directly with Cell objects. For example, here's
a temperature converter implemented directly with cells:

The trellis.Cell() constructor takes three arguments: a zero-argument
callable (or None), an optional value, and an optional "discrete" flag.
In our example above, we created a pair of cells with both rules and values,
that are not discrete.

Notice, by the way, that when you are directly creating cells, you must use
zero-argument callables. That is, Cell objects don't pass in a "self" argument
to their rules. (The reason rules in a component use a "self" is that those
rules are turned into methods before the cell is created. The Cell doesn't
pass in a "self", but it's already bound to the method, so it shows up anyway.)

The value attribute of a Cell can be read or set, to get or change the
value of the cell, and it works just like getting or setting a component cell
attribute (except that setting a cell's value to another cell doesn't cause the
cell to be replaced!). In addition to the .value attribute, there are also
get_value() and set_value() methods:

>>> C.set_value(-40)
>>> F.get_value()
-40.0

These can be useful if you need to register callbacks with other systems. For
example, you could use a cell's set_value() method as a callback for a
Twisted "deferred" object, so that the cell would receive the deferred's value
when it became available.

Here's our earlier "noise filter" example, reconstituted as a set of cells:

As you can see, you can provide either a value only, or a rule and a value when
you create a cell. However, if you provide just a rule and no value, you end
up with a read-only cell whose value can't be set:

What the above means is that you have a read-only cell whose current value is
None, but has not yet been initialized. This means that if you actually
try to read the value of this cell, it may or may not match what the
repr() showed. (This is because simply looking at the cell shouldn't
cause the cell's value to be calculated; that could be very painful when
debugging).

If we actually read the value of this cell, the rule will be run:

>>> roc.value
123

But since the rule doesn't depend on any other cells, the cell changes type
again, to a Constant:

>>> roc
Constant(123)

Since the rule didn't depend on any other cells, there is never any way that
it could be meaningfully recalculated. Thus, it becomes constant, and cannot
be listened-to by any other rules. If we create another rule that reads this
cell, it will not end up depending on it:

Thus, constant values propagate automatically through the cell network,
eliminating dependencies on things that can't possibly change. Of course, if a
read-only cell depends on a cell that can change, it remains a read-only
cell, and will be recalculated whenever its dependencies change:

Note that you can take advantage of constant propagation by explicitly setting
a component attribute to a trellis.Constant at creation time. For example,
if for some reason you wanted a temperature converter that could only be used
once:

(This would probably be more useful with something like the NoiseFilter
example, in that you could set its threshhold to a Constant(),
eliminating the need for the filtered rule to check for changes to the
threshhold in order to know if it should be recalculated.)

In the case of a Component, this data is also stored in the component's
__cells__ attribute:

>>> trellis.Cells(view) is view.__cells__
True

This makes it possible for you to set up direct links between components using
shared cells. It also lets you access cell objects directly, in order to e.g.
register their set_value() methods as callbacks for other systems.

As you can see, the value a discrete cell is created with, is the default value
it resets to between set (or calculated) values. If you want to make a
resetting rule, just include a rule in addition to the default value and the
discrete flag.

The Performer constructor takes only one parameter: a zero-argument callable,
such as a bound method or a function with no parameters. You can't set a value
for a Performer (because it's not writable), nor can you make it discrete
(since that would imply a readable value, and performer cells exist only for
their side-effects). Creating a Performer cell schedules it for execution as
soon as the current modifier is complete and any normal rules are finished. It
will then be re-executed in the future, after any cells or other trellis-
managed data structures it depended on are changed. (As long as the
Performer isn't garbage collected, of course.)

Cells keep strong references to all of the cells whose values they accessed
during rule calculation, and weak references to all of the cells that accessed
them. This ensures that as long as a listener exists, its most-recently
read subject(s) will also continue to exist.

Cells whose rules are effectively methods (i.e., cells that represent component
attributes) also keep a strong reference to the object that owns them, by
way of the method's im_self attribute. This means that as long as some
attribute of a component is being observed, the whole component will continue
to exist.

In addition, a component's __cells__ dictionary keeps a reference to all
its cells, creating a reference cycle between the cells and the component.
Thus, Component instances can only be reclaimed by Python's cycle collector,
and are not destroyed as soon as they go out of scope. You should therefore
avoid giving Component objects a __del__ method, and should explicitly
dispose of any resources that you want to reclaim early.

You should NOT, however, attempt to break the cycle between a component and its
cells. If the cells have any observers, this will just cause the rules to
break upon recalculation, or else recreate some of the cells, depending on how
you tried to break the cycle. It's better to simply let Python detect the
cycle and get rid of it itself.

However, if you absolutely MUST mess with this, the best thing to do is delete
the component's __cells__ attribute with delob.__cells__, as this will
ensure that any dangling observers will at least get attribute errors when
recalculation occurs. Thus, if the component is really still in use, at least
you'll get an error message, instead of weird results. But it still won't be a
fun problem to debug, so it's highly recommended that you leave the garbage
collection to Python. Python always knows more about what's happening in your
program than you do!

The "Trellis" name comes from Dr. David Gelernter's 1991 book, "Mirror Worlds",
where he describes a parallel programming architecture he called "The Trellis".
In the excerpted passages below, he describes the portions of his architecture
that are roughly the same as in this Python implementation:

"Consider an upward-stretching network of infomachines tethered together,
rung-upon-rung (billowing slightly in the breeze?) No two rungs need have
exactly the same number of machines.... There might be ten rungs in all or
hundreds or thousands, and the average rung might have anywhere from a
handful to hundreds of members. This architecture spans a huge range of
shapes and sizes....

So, these things are "tethered together" -- meaning? Those lines are
lines of communication. Each member of the Trellis is tethered to some
lower-down machines and to some higher-ups.... A machine deals only with
the machines to which it is tethered. So far as it's concerned, the rest
don't exist. It deals with inferiors in a certain way and superiors in a
certain other way, and that's it....

Information rushes upward through the network, and the machines on each
rung respond to it on their own terms.... Each machine focuses on one
piece of the problem -- on answering a single question about the thing out
there...that is being monitored. Each machine's entire and continuous
effort is thrown into answering its one question. You can query a machine
at any time -- what's the current best answer to your particular question?
-- and it will produce an up-to-the-second response....

So data flows upward through the ensemble; there's also a reverse, downward
flow of what you might call "anti-data" -- inquiries about what's going
on. A high ranking element might attempt to generate a new value, only to
discover it's missing some key datum from an inferior. It sends a query
downward.... The inferior tries to come up with some new data.... If a
bottom-level machine is missing data,.... It can ask the outside world
directly for information....

The fact that data flows up and anti-data flows downwards means that, in a
certain sense, a Trellis can run either forwards or backwards, or both at
the same time....

A Trellis, it turns out, is a lot like a crystal.... When you turn it on,
it vibrates at a certain frequency.

Meaning? In concept, each Trellis element is an infomachine. All these
infomachines run separately and simultaneously.

In practice, we do things somewhat differently....

We run the Trellis in a series of sweeps. During the first sweep, each
machine gets a chance to [produce one output value]. During the second,
each [produces a second value], and so on. No machine [produces] a
second [value] until every [machine] has [produced] a first [value]."

While Dr. Gelernter's Trellis was designed to be run by an arbitary number of
parallel processors, our Trellis is scaled down to run in a single Python
thread. But on the plus side, our Trellis automatically connects its "tethers"
as it goes, so we don't have to explicitly plot out an entire network of
dependencies, either!

Ken Tilton's "Cells" library for Common Lisp inspired the implementation of
the Trellis. While Tilton had never heard of Gelernter's Trellis, he
independently discovered the value of having synchronous updates, like the
"sweeps" of Gelernter's design, and combined them with automatic dependency
detection to create his "Cells" library.

I heard about this library only because Google sponsored a "Summer of Code"
project to port Cells to Python - a project that produced the PyCells
implementation. My implementation, however, is not a port but a re-visioning
based on native Python idioms and extended to handle mutually recursive rules,
side-effects, rollback, and various other features that do not precisely map
onto the features of Cells, PyCells, or other Python frameworks inspired by
Cells (such as "Cellulose").

While the first very rough drafts of this package were done in 2006 on my own
time, virtually all of the work since has been generously funded by OSAF, the
Open Source Applications Foundation.

Debugging code that does modifications can be difficult because it can be
hard to know which cells are which. There should be a way to give cells
an identifier, so you know what you're looking at.

Currently, there's no protection against accessing Cells from other
threads, nor support for having different logical tasks in the same thread
with their own contexts, services, etc. This should be fixed by using
the "Contextual" library to manage thread-local (and task-local) state for
the Trellis, and by switching to the appropriate context.State whenever
non-rule/non-modifier code tries to read or write a cell.

There should probably be an easier way to reference cells directly, instead
of using Cells(ob)['name'] -- perhaps a .link property, similar to the
.future of "todo" cells, would make this easier.

The poll() and repeat() functions are undocumented in this release.

It's a bad idea to use on_commit() for user-level operations

TrellisDB

A system for processing relational-like records and "active queries" mapped
from zero or more backend storage mechanism.

TrellisUI

Framework for mapping application components to UI views.

Widget specification, styling, and layout system that's backend-agnostic,
ala Adobe's "Eve2" layout constraint system. Should be equally capable of
spitting out text-mode drawings of a UI, as it is of managing complex wx
"GridBagSizer" layouts.